Computer-Assisted Aircraft Anti-Icing Fluids Endurance Time Determination
Bibliographic record
Abstract
Deicing and anti-icing the aircraft using proper chemical fluids, prior takeoff, are mandatory. A thin layer of ice or snow can compromise the safety, causing lift loss and drag increase. Commercialized deicing and anti-icing fluids all pass a qualification process which is described in Society of Automotive Engineering (SAE) documents. Most of them are endurance time tests under freezing and frozen contaminants, under simulated and natural conditions. They all have in common that the endurance times have to be determined by visual inspection. When a certain proportion of the test plate is covered with contaminants, the endurance time test is called. In the goal of minimizing human error resulting from visual inspection and helping in the interpretation of fluid failure, help-decision computer-assisted algorithms have been developed and tested under different conditions. The algorithms are based on common image processing techniques. The algorithms have been tested under three different icing conditions, water spray endurance test, indoor snow test and light freezing rain tests, and were compared to the times determined by three experimented technicians. A total of 14 tests have been compared. From them, 11 gave a result lower than 5% of the results given by the technicians. In conclusion, the computer-assisted algorithms developed are efficient enough to support the technicians in their failure call. However, further works need to be performed to improve the analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".